1. Field of the Invention
The present invention relates to automated reasoning. More particularly, the present invention relates to using statistical techniques in order to enable automated reasoning with noisy data.
2. Description of Background Art
Robots will soon be expected to accomplish tasks within their environments and to satisfy the perceived requests and desires of their users. Determining these desires and reasoning about how they can be accomplished requires common sense. One of the main challenges in the field of robotics is making robots more intelligent by endowing them with common sense. In order for robots to have common sense, they need some amount of general knowledge to use as a basis. They can then reason from this basis to accomplish their tasks and interact with their world intelligently.
Automated reasoning, a topic within the field of artificial intelligence, is concerned with using computers to perform logical reasoning. Logical reasoning, such as deduction, consists of drawing conclusions from facts. Conclusions are formed by applying rules (e.g., implications) to facts. This is also known as inferencing. Logical reasoning systems comprise implications, a basis of facts, and control mechanisms for applying implications to these facts. In automated reasoning systems, facts and implications are stored in a “knowledge base.” Facts are established by, for example, user input (e.g., by typing in “The stove is hot”) or sensor input (e.g., by placing a heat sensor near the stove). Implications are established by, for example, user input (e.g., by typing in “An item in a refrigerator is cold”) or machine learning.
For logical reasoning to be sound, an implication must require that its conclusion follow inevitably from the fact from which it is drawn. Depending on the facts and implications that a system starts with, many different conclusions can be reached. In addition, implications can be applied to both facts (originally input into the system) and conclusions (generated by implications). Because of the interactions between facts, implications, and conclusions, it is important that the initially-input facts and implications be correct. If the facts or implications are false or inconsistent with one another, the resulting conclusions may be invalid.
Thus, automated reasoning systems must have self-consistent knowledge bases. Any sound and complete consistency check will require an exponential amount of time based on the number of items in the database. Consistency checks need to be performed each time something is added the knowledge base. The result is that automated reasoning systems are very brittle. Their knowledge bases are frequently handcrafted, and differences in vocabulary are usually not tolerated.
One way to reason with inconsistent knowledge is to revise the knowledge base before performing the reasoning. Any revision process requires, either explicitly or implicitly, the ordering of rules based on priority. This has been proved by “On the semantics of updates in database” by R. Fagin, J. D. Ullman, M. Y. Vardi, in Principles of Database Systems: Proceedings of the Second ACM SIGACT-SIGMOD Symposium, pp. 352-365, Atlanta, 1983; and “Knowledge in Flux—Modeling the Dynamic of Epistemic States” by P. Gardenfors, Cambridge, Mass., 1988. Alternatively, one can reason with inconsistent knowledge without first revising the knowledge base, as discussed in “How to infer from inconsistent beliefs without revising?” by S. Benferhat, D. Dubois, and H. Prade, Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Montreal, Canada, Aug. 20-25, 1995. In general, prior work in this area has been based on logical artificial intelligence techniques rather than on statistical techniques.
Before inferencing from a knowledge base, it is also useful to “compile” the knowledge base. Compilation of a knowledge base can comprise cleaning, generalizing, and compacting the data in the knowledge base. Compilation can decrease the amount of resources (time, memory, etc.) required by the reasoning process.
What is needed is a way to enable automated reasoning systems to use noisy data, that is, knowledge bases that are not self-consistent. In addition, automated reasoning systems should be able to handle tasks that contain words that are not in the knowledge base.